Self-organised Aggregation in Swarms of Robots with Informed Robots

  • Ziya Firat
  • Eliseo Ferrante
  • Nicolas Cambier
  • Elio TuciEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)


In this paper, we study a swarm of robots that has to select one aggregation site in an environment in which two sites are available. It is known in the literature that, in presence of asymmetries in the environment, robot swarms are able to perform a collective choice and aggregate in one among two possible sites, for example the largest of the two. We focus on an aggregation scenario where the environment is morphologically symmetric. The two aggregation sites are identical with only one exception: their colour. In addition, in the swarm only a proportion of robots, that we call the informed robots, possess extra information concerning on which specific site the swarm is required to aggregate. The rest of the robots are non-informed, thus they do not possess the above mentioned extra information. In simulation-based experiments we show that, if no robot in the swarm is informed, the swarm is able to break the symmetry and aggregates on one of the two sites at random. However, the introduction of a small proportion of informed robots is enough to break the symmetry: the majority of the swarm aggregates on the site preferred by the informed robot. Additionally, the swarm is also able to completely aggregate on one of the two sites when only 30% of the robots are informed, independently from the swarm size among those we considered. Finally, we analyse how the time dynamics of the aggregation process depend on the proportion of informed robots.


Swarm intelligence Swarm robotics Self-organisation Aggregation Informed leaders 


  1. 1.
    Alkilabi, M., Narayan, A., Tuci, E.: Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies. Swarm Intell. 11(3–4), 185–209 (2017)CrossRefGoogle Scholar
  2. 2.
    Bayindir, L., Şahin, E.: Modeling self-organized aggregation in swarm robotic systems. In: IEEE Swarm Intelligence Symposium, SIS 2009, pp. 88–95. IEEE (2009)Google Scholar
  3. 3.
    Bonani, M., et al.: The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4187–4193 (2010)Google Scholar
  4. 4.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  5. 5.
    Camazine, S.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2003)zbMATHGoogle Scholar
  6. 6.
    Cambier, N., Frémont, V., Trianni, V., Ferrante, E.: Embodied evolution of self-organised aggregation by cultural propagation. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 351–359. Springer, Cham (2018). Scholar
  7. 7.
    Campo, A., Garnier, S., Dédriche, O., Zekkri, M., Dorigo, M.: Self-organized discrimination of resources. PLoS ONE 6(5), e19888 (2010)CrossRefGoogle Scholar
  8. 8.
    Çelikkanat, H., Şahin, E.: Steering self-organized robot flocks through externally guided individuals. Neural Comput. Appl. 19(6), 849–865 (2010)CrossRefGoogle Scholar
  9. 9.
    Correll, N., Martinoli, A.: Modeling and designing self-organized aggregation in a swarm of miniature robots. Int. J. Robot. Res. 30(5), 615–626 (2011)CrossRefGoogle Scholar
  10. 10.
    Couzin, I., Krause, J., Franks, N., Levin, S.: Effective leadership and decision making in animal groups on the move. Nature 433, 513–516 (2005)CrossRefGoogle Scholar
  11. 11.
    Deneubourg, J., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. Biol. Bull. 202(3), 262–267 (2002)CrossRefGoogle Scholar
  12. 12.
    Dorigo, M., et al.: Evolving self-organizing behaviors for a swarm-bot. Auton. Robot. 17(2), 223–245 (2004)CrossRefGoogle Scholar
  13. 13.
    Ferrante, E., Turgut, A.E., Huepe, C., Stranieri, A., Pinciroli, C., Dorigo, M.: Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt. Behav. 20(6), 460–477 (2012)CrossRefGoogle Scholar
  14. 14.
    Ferrante, E., Turgut, A., Stranieri, A., Pinciroli, C., Birattari, M., Dorigo, M.: A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat. Comput. 13(2), 225–245 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Garnier, S., et al.: The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif. Life 14(4), 387–408 (2008)CrossRefGoogle Scholar
  16. 16.
    Garnier, S., et al.: Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 169–178. Springer, Heidelberg (2005). Scholar
  17. 17.
    Garnier, S., Gautrais, J., Asadpour, M., Jost, C., Theraulaz, G.: Self-organized aggregation triggers collective decision making in a group of cockroach-like robots. Adapt. Behav. 17(2), 109–133 (2009)CrossRefGoogle Scholar
  18. 18.
    Gauci, M., Chen, J., Li, W., Dodd, T., Groß, R.: Self-organized aggregation without computation. Int. J. Robot. Res. 33(8), 1145–1161 (2014)CrossRefGoogle Scholar
  19. 19.
    Hauert, S., Winkler, L., Zufferey, J., Floreano, D.: Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intell. 20(2–4), 167–188 (2008)CrossRefGoogle Scholar
  20. 20.
    Jeanson, R., Rivault, C., Deneubourg, J., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.: Self-organized aggregation in cockroaches. Anim. Behav. 69(1), 169–180 (2005)CrossRefGoogle Scholar
  21. 21.
    Kato, S., Jones, M.: An extended family of circular distributions related to wrapped cauchy distributions via brownian motion. Bernoulli 19(1), 154–171 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M.: Human interaction with robot swarms: a survey. IEEE Trans. Hum. Mach. Syst. 46(1), 9–26 (2016). Scholar
  23. 23.
    Montes de Oca, M., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., Dorigo, M.: Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell. 5(3–4), 305–327 (2011)CrossRefGoogle Scholar
  24. 24.
    Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRefGoogle Scholar
  25. 25.
    Pini, G., Brutschy, A., Frison, M., Roli, A., Dorigo, M., Birattari, M.: Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intell. 5(3–4), 283–304 (2011)CrossRefGoogle Scholar
  26. 26.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). Scholar
  27. 27.
    Sperati, V., Trianni, V., Nolfi, S.: Self-organised path formation in a swarm of robots. Swarm Intell. 5(2), 97–119 (2011)CrossRefGoogle Scholar
  28. 28.
    Tuci, E., Alkilabi, M., Akanyety, O.: Cooperative object transport in multi-robot systems: a review of the state-of-the-art. Front. Robot. AI 5, 1–15 (2018)CrossRefGoogle Scholar
  29. 29.
    Tuci, E., Rabérin, A.: On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario. Swarm Intell. 9(4), 267–290 (2015)CrossRefGoogle Scholar
  30. 30.
    Valentini, G., Ferrante, E., Dorigo, M.: The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Front. Robot. AI 4, 9 (2017). Scholar
  31. 31.
    Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Auton. Agents Multi Agent Syst. 30(3), 553–580 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The Department of Computer ScienceMiddlesex UniversityLondonUK
  2. 2.School of Computer ScienceUniversity of BirminghamDubaiUnited Arab Emirates
  3. 3.Heudiasyc UMR CNRS 7253, Université de Technologie de CompiégneCompiégneFrance
  4. 4.Faculty of Computer ScienceUniversité de NamurNamurBelgium

Personalised recommendations